Measurements and analysis of multistatic and multimodal micro-Doppler signatures for automatic target classification

The purpose of this paper is to present an experimental trial carried out at the Defence Academy of the United Kingdom to measure simultaneous multistatic and multimodal micro-Doppler signatures of various targets, including humans and flying UAVs. ewline Signatures were gathered using a network of sensors consisting of a CW monostatic radar operating at 10 GHz (X-band) and an ultrasound radar with a monostatic and a bistatic channel operating at 45 kHz and 35 kHz, respectively. A preliminary analysis of automatic target classification performance and a comparison with the radar monostatic case is also presented

Train Monitoring using GSM-R Based Passive Radar

Train detection technologies are universal to all modern railway signal and control systems. They are essential for managing the movement of vehicles across entire transport networks, and to ensure their safe operation. In this paper we investigate the feasibility of a new train monitoring capability based on passive radar technology. The system exploits signal transmissions from the railways’ GSM-Railway (GSM-R) radio communications infrastructure, and has the potential to determine the positions and velocities of trains over any section of a railway network where there is GSM-R coverage. A theoretical ambiguity function analysis on directly measured GSM-R waveforms suggest that targets can be detected with axial range resolutions of approximately 850 m, and velocities down to less than 1 mph. To demonstrate proof-of-concept, a series of experiments were carried out using a software-defined GSM-R passive radar system. The results show the first detections of trains at bistatic ranges of just over 1 km moving at various speeds. There are now hundreds of thousands of miles of railway track covered by GSM-R globally, with many more countries planning to rollout systems nationally. The results therefore imply that GSM-R based passive radar technology could be used to develop low-cost train monitoring capabilities worldwide alongside the existing GSM-R radio communications infrastructure

Activity Recognition Based on Micro-Doppler Signature with In-Home Wi-Fi

Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for inhome Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method

Indoor target tracking using high doppler resolution passive Wi-Fi radar

This paper describes two Doppler only indoor passive Wi-Fi tracking methods based on high Doppler resolution passive radar. Two filters are investigated in this paper, the extended Kalman filter and the sequential importance resampling (SIR) particle filter. Experimental results for these two tracking filters are presented using results from software defined passive Wi-Fi radar using a standard 802.11 access point as an illuminator. The experimental results show that the SIR particle filter performs well using Wi-Fi signals for indoor tracking with a high degree of accuracy. Proposals for simplifying the SIR particle and application to multiple target tracking are also discussed

Signs of life detection using wireless passive radar

Non-contact devices for monitoring signs of life have attracted a lot of attention in recent years for applications in security, emergency and disaster situations. Current devices however, generally utilize bespoke active systems to transmit large bandwidth signals. In this paper, a real-Time phase extraction method based on passive Wi-Fi radar is proposed for detecting the chest movements associated with a person breathing. Since the monitored movements are of low amplitude and small Doppler shift, this method uses the phase variation rather than traditional range-Doppler processing. The processing is based on time domain cross correlation, with the addition of a Hampel filter for outlier detection and removal. In this paper the basic passive Wi-Fi model and limitations of traditional cross ambiguity function for signs of life detection are first introduced. The phase extraction method is then described followed by experimental results and analysis. Detection of breathing for a stationary person is shown in both in-room and through wall scenarios using both the Wi-Fi beacon and data transmissions. This is believed to be the first demonstration of signs of life detection using phase extraction in passive radar and extends the capability of such systems into a wide range of new applications

A low-cost through-the-wall FMCW radar for stand-off operation and activity detection

In this paper we present a new through-wall (TW) FMCW radar system. The architecture of the radar enables both high sensitivity and range resolutions of <1.5 m. Moreover, the radar employs moving target indication (MTI) signal processing to remove the problematic primary wall reflection, allowing higher signal-to- noise and signal-to-interference ratios, which can be traded-off for increased operational stand-off. The TW radar operates at 5.8 GHz with a 200 MHz bandwidth. Its dual-frequency design minimises interference from signal leakage, and permits a baseband output after deramping which is digitized using an inexpensive 24-bit off-the-shelf sound card. The system is therefore an order of magnitude lower in cost than competitor ultrawideband (UWB) TW systems. The high sensitivity afforded by this wide dynamic range has allowed us to develop a wall removal technique whereby high-order digital filters provide a flexible means of MTI filtering based on the phases of the returned echoes. Experimental data demonstrates through-wall detection of individuals and groups of people in various scenarios. Target positions were located to within ±1.25 m in range, allowing us distinguish between two closely separated targets. Furthermore, at 8.5 m standoff, our wall removal technique can recover target responses that would have otherwise been masked by the primary wall reflection, thus increasing the stand-off capability of the radar. Using phase processing, our experimental data also reveals a clear difference in the micro-Doppler signatures across various types of everyday actions

Physical Activity Sensing via Stand-Alone WiFi Device

WiFi signals for physical activity sensing shows promising potential for many healthcare applications due to its potential for recognising various everyday activities, non-invasive nature and low intrusion on privacy. Traditionally, WiFi-based sensing uses the Channel State Information (CSI) from an offthe- shelf WiFi Access Point (AP) which transmits signals that have high pulse repetition frequencies. However, when there are no users on the communication network only beacon signals are transmitted from the WiFi AP which significantly deteriorates the sensitivity and specificity of such systems. Surprisingly WiFi based sensing under these conditions have received little attention given that WiFi APs are frequently in idle state. This paper presents a practical system based on passive radar technique which does not require any special setup or preset firmware and able to work with any commercial WiFi device. To cope with the low density of beacon signal, a modified Cross Ambiguity Function (CAF) has been proposed to reduce redundant samples in the recorded. In addition, an external device has been developed to send WiFi probe request signals and stimulate an idle AP to transmit WiFi probe responses thus generate usable transmission signals for sensing applications without the need to authenticate and join the network. Experimental results prove that proposed concept can significantly improve activity detection and is an ideal candidate for future healthcare and security applications

Passive WiFi Radar for Human Sensing Using A Stand-Alone Access Point

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in ehealthcare, security and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of Channel State Information (CSI) data which originates from commodity WiFi Access Points (AP) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate OFDM signals, or periodic WiFi beacon signals whilst in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, whilst a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In the paper we present experimental data which verifies our proposed methods for using any type of signal transmission from a stand-alone WiFi device, and demonstrate the capability for human activity sensing

Respiration and Activity Detection based on Passive Radio Sensing in Home Environments

The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling